By hinging on the assumption that a system to be controlled is fully unknown, many data-driven control approaches do not leverage available or readily inferable priors. In contrast to this viewpoint, this paper analyzes the impact of using the system's equilibrium subspace to inform direct and indirect linear quadratic...
Yi-Chun Liao, Valentina Breschi, M. Nicotra· 0 citations
This analysis demonstrates that leakage effects are the main driver for data to represent the dynamics of the system and that data-enabled predictive controllers built on datasets with insufficient bandwidth, despite fulfilling standard rank conditions, suffer from severe ill-conditioning and fail to achieve reference...
G. Giacomelli, Chuyu Lu, Siep Weiland et al.· arXiv.org· 0 citations
This paper presents a subspace data-driven predictive control method for linear parameter-varying (LPV) systems. Starting from an affine LPV state-space model in innovation form, we derive a multi-step predictor that separates the effects of past data, future inputs, scheduling trajectories, and innovations. By project...
Federico Porcari, C. Verhoek, V. Breschi et al.· arXiv.org· 0 citations
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